Semantic Enhancement of Social Tagging Systems

  • Fabian Abel
  • Nicola Henze
  • Daniel Krause
  • Matthias Kriesell
Part of the Annals of Information Systems book series (AOIS, volume 6)


Social tagging systems have shown an impressive potential for information discovery and exploration. Enriched with Semantic Web technologies, they enable to tap valuable metadata about Web resources and to detect hidden relations, thus, to capture information about both content and context of the resources. In this article, we propose a novel way to combine semantic technologies with Web 2.0 paradigms. We introduce the GroupMe! system, which extends current social tagging systems by giving users more flexibility in organizing and maintaining Web content. In GroupMe!, users can create groups of Web resources they consider relevant by simple drag & drop operations. They can tag and share their groups and Web content with fellow users and benefit from improved search and retrieval capabilities. We evaluate the GroupMe! approach and investigate on the effect of grouping resources for search in tag-based social systems. Our experiments show that the quality of search result ranking can be significantly improved by introducing and exploiting the grouping of resources.


Ranking Algorithm Group Context Ranking Strategy Social Bookmark Zoomable Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Nicole Ullmann, Mischa Frank, Daniel Plappert, Patrick Siehndel, and Zhivko Asenov for their contribution and engagement in realizing the GroupMe! system.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Fabian Abel
    • 1
  • Nicola Henze
    • 1
  • Daniel Krause
    • 1
  • Matthias Kriesell
    • 2
  1. 1.IVS – Semantic Web GroupLeibniz University HannoverHannoverGermany
  2. 2.Department of MathematicsUniversity of HamburgHamburgGermany

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